The UNCOVER Survey: First Release of Ultradeep JWST/NIRSpec PRISM Spectra for ∼700 Galaxies from <i>z</i> ∼ 0.3–13 in A2744
Bibliographic record
Abstract
Abstract We present the design and observations of low-resolution JWST/NIRSpec PRISM spectroscopy from the Ultradeep NIRSpec and NIRCam ObserVations before the Epoch of Reionization (UNCOVER) Cycle 1 JWST Treasury program. Targets are selected using JWST/NIRCam photometry from UNCOVER and other programs, and cover a wide range of categories and redshifts to ensure the legacy value of the survey. These categories include the first galaxies at z ≳ 10, faint galaxies during the Epoch of Reionization (z ∼ 6−8), high-redshift active galactic nuclei (z ≳ 6), Population III star candidates, distant quiescent and dusty galaxies (1 ≲ z ≲ 6), and filler galaxies sampling redshift–color–magnitude space from z ∼ 0.1−13. Seven NIRSpec microshutter array masks across the extended A2744 cluster were observed, along with NIRCam parallel imaging in nine filters (F090W, F115W, F150W, F200W, F277W, F356W, F410M, F444W, and F480M) over a total area of ∼26 arcmin2, overlapping existing Hubble Space Telescope coverage from programs including the Hubble Frontier Fields and BUFFALO. We successfully observed 553 objects down to m F444W ∼ 30 AB, and by leveraging mask overlaps, we reach total on-target exposure times ranging from 2.4 to 16.7 hr. We demonstrate the success rate and distribution of the confirmed redshifts, and also highlight the rich information revealed by these ultradeep spectra for a subset of our targets. An updated lens model of A2744 is also presented, including 14 additional spectroscopic redshifts and finding a total cluster mass of M SL = (2.1 ± 0.3) × 1015 M ⊙. We publicly release reduced 1D and 2D spectra for all objects observed in summer 2023 along with a spectroscopic redshift catalog and the updated lens model of the cluster ( https://jwst-uncover.github.io/DR4.html ).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".